Simulating User Intervention for Interactive Semantic Place Recognition with Mobile Devices

T. Lovett, E. O'Neill

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

1 Citation (Scopus)

Abstract

Recognising and learning users' semantically meaningful places is useful for personalising services and recommender systems, particularly in a mobile environment. Existing approaches that use mobile devices focus on automating place inference from underlying data, i.e. with little user interaction and intervention { where user feedback is incorporated into the inference process. The process of intervention can be burdensome to the user but, without intervention, it is difficult to both capture personal place semantics and up-date places over time; resulting in a trade-off between system performance and user burden. In this paper, we present early results from a place recognition and learning approach that relies on user intervention as a form of active learning. Using simulations of user intervention generated from fine-grained ground truth, we show that good place semantic capture, classification and learning performance can feasibly be achieved in real time on mobile devices with only a small amount of user intervention.
Original languageEnglish
Title of host publicationLocalPeMA'12: Proceedings of the 2012 RecSys Workshop on Personalizing the Local Mobile Experience
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages13-18
Number of pages6
DOIs
Publication statusPublished - 2012
EventRecSys Workshop on Personalizing the Local Mobile Experience - Dublin, Ireland
Duration: 13 Sep 201213 Sep 2012

Conference

ConferenceRecSys Workshop on Personalizing the Local Mobile Experience
CountryIreland
CityDublin
Period13/09/1213/09/12

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Lovett, T., & O'Neill, E. (2012). Simulating User Intervention for Interactive Semantic Place Recognition with Mobile Devices. In LocalPeMA'12: Proceedings of the 2012 RecSys Workshop on Personalizing the Local Mobile Experience (pp. 13-18). New York: Association for Computing Machinery. https://doi.org/10.1145/2365946.2365950